Fig 1.
Schematic diagram representing the different steps for the construction of Marine Debris Archive-MARIDA.
Table 1.
Collected Marine Debris reports across different countries and continents for the period 2015–2021.
The table shows the regions along with the reported events information (source, date and exact location).
Fig 2.
The sites (red dots in the map) where Marine Debris events were reported, and corresponding Sentinel-2 satellite images were acquired and processed.
Marine Debris and other features that co-existed were annotated in considered satellite data. The corresponding map is acquired from Natural Earth (http://www.naturalearthdata.com/).
Fig 3.
The spectral signatures of the Marine Debris and Natural Organic Material classes derived from the annotations with the high confidence levels.
The mean spectral signatures are presented with 25–75 percentiles as error bars.
Fig 4.
A 2D embedding using T-SNE algorithm with SAM metric for the classes: Marine Debris, Ships, Sparse Sargassum, Natural Organic Material and Waves.
Each class is represented with a different color. Different symbols demonstrate the confidence level of annotations.
Table 2.
The thematic classes of MARIDA.
Name, description and corresponding number of patches are presented for each class. All acronyms are stated here.
Table 3.
MARIDA’s class distribution at pixel-level.
For Sentinel-2 tiles description, the reader is referred to Table 1. For classes acronyms, the reader is referred to Table 2.
Table 4.
Evaluation scores obtained by RFSS, RFSS+SI, RFSS+SI+GLCM and U-Net for each class on Marine Debris Archive.
The highest scores are highlighted. All acronyms are stated in Table 2.
Fig 5.
Classification results extracted by the baseline RFSS+SI+GLCM and U-Net models.
Selected indicative cases demonstrate (A) S2_12-12-20_16PCC_6, (B) S2_22-12-20_18QYF_0, (C) S2_27-1-19_16QED_14 and (D) S2_14-9-18_16PCC_13 patches on test set. RGB patches are derived from Sentinel-2 data which were freely downloaded from https://earthexplorer.usgs.gov/. All acronyms are stated in Table 2.
Fig 6.
Features importance using permutation on RFSS+SI+GLCM model.
Each feature represents a different highly correlated group. The largest mean pixel accuracy decrease occurs by permuting CON, NDWI, NDVI and FDI.